Abstract: The traditional principal component analysis (PCA) similarity factor method does not make full use of the higher-order statistics of the process data,which results in degraded fault identification performance.In order to solve this problem,a statistics pattern analysis (SPA) similarity factor method is proposed in this paper.Firstly,the original process data are transformed into the statistics space by using the SPA.Then,the PCA is adopted to obtain the principal component directions in the statistics space.Finally,the similarity between the principal components is calculated to identify faults.Simulation results on the continuous stirring tank reactor (CSTR) process show that the proposed SPA similarity factor method is more effective than the traditional PCA similarity factor method in terms of identifying faults.

Key words: statistics pattern analysis, PCA similarity factor, fault detection, fault identification

摘要： 传统的主元分析（PCA）相似系数法没有充分利用数据的高阶统计量等有用的过程信息，导致故障识别效果较差。针对此问题，提出一种统计量模式分析（SPA）相似系数法。该方法首先使用SPA将原始数据转换到统计量空间中，然后在统计量空间中利用PCA获取主元方向，计算主元之间的相似性识别故障。在连续搅拌反应器（CSTR）过程上的仿真结果说明提出的SPA相似系数法比传统的PCA相似系数法能更有效地识别故障。

关键词: 统计量模式分析, PCA相似系数, 故障检测, 故障识别